display(Image(filename='1DTime.jpg'))
Data Type 1: 1D Time Series¶
The figure above is a 1D time series because it displays the change of CO2 levels in the atmosphere over the last 800 thousand years, or in other words, time. It is 1 dimensional becuase it is only measuring CO2 levels at one point in time, without other local considerations or things that would add dimensionality.
display(Image(filename='2DScalar.png'))
Data Type 2: 2D Scalar Field¶
The figure above is your typical temperature map for the United States' major cities. It represents a 2D scalar field because every location (latitude and longitude coordinates) has an associated temperature value.
display(Image(filename='3DScalar.png'))
Data Type 3: 3D Scalar Field¶
The visualization above is a 3D model of the earth without its oceans. It depicts a 3D scalar field because every point on the geoid has a height in meters associated with it. It corresponds to a 3D coordinate system (x,y,z).
display(Image(filename='2DVector.png'))
Data Type 4: 2D Vector Field¶
The visualization above depicts a 2D vector field for Greens Theorem, which gives the relationship between a line integral around a simple closed curve C and a double integral over the plane region D bounded by C. This is a 2D vector field because for every point in the space x, y a there is an associated vector quantity.
display(Image(filename='3DShape.jpg'))
Data Type 5: 3D Shape¶
The figure above is a 3D shape and has an associated polygon format model file (PYL). This file contains points for a 3D space which makes up the image. A PLY file consists of a list of vertices and then a list of polygons. The header specifies how many vertices and polygons are in the file, and also states what properties are associated with each vertex, such as (x,y,z) coordinates, normals and color.
display(Image(filename='Graph.jpg'))
Data Type 6: Graph¶
The image above depicts The human disease network. A disorder (circle) and a gene are connected if the gene is implicated in the disorder. The size of the circle represents the number of distinct genes associated with the disorder. This is an example of a bipartite graph because the vertices can be divided into two disjoint sets U and V such that every edge connects a vertex in U to one in V.